Power system load flow studies are essential for lightning protection studies analyzing the steady-state behavior of power networks. Traditionally, these studies have focused on a single objective, such as minimizing power losses or ensuring voltage regulation. However, in modern power systems, there are often multiple, often conflicting, objectives that must be considered, including economic, environmental, and reliability factors. This paper presents a multiobjective optimization approach for power system load flow studies that can simultaneously optimize these diverse objectives.
The proposed method utilizes a weighted-sum approach to formulate the
multiobjective optimization problem, with objectives including:
Minimizing total system power losses
Minimizing environmental impact (e.g., CO2 emissions)
Maximizing system reliability (e.g., minimizing overloading of transmission lines)
The optimization is subject to power system constraints such as voltage limits, line capacity limits, and generator output limits.
The multiobjective optimization problem is solved using a genetic algorithm-based approach, which can efficiently explore the trade-offs between the competing objectives. The method is tested on a standard IEEE 30-bus test system, and the results demonstrate the effectiveness of the approach in identifying optimal load flow solutions that balance economic, environmental, and reliability considerations.
The key contributions of this work include:
Formulation of a comprehensive multiobjective optimization framework for power system load flow studies
Incorporation of economic, environmental, and reliability objectives
Application of a genetic algorithm-based solution method to explore the Pareto-optimal solutions
Demonstration of the approach on a standard test system
The findings of this research can help power system planners and operators make more informed decisions that consider the multiple,hv transformer testing often conflicting, objectives in power system operation and planning.
